{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from res2net_Fca import *\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from tools import *\n",
    "from torch.optim import lr_scheduler\n",
    "import math\n",
    "import sys\n",
    "import time\n",
    "from torchvision.transforms import RandAugment\n",
    "import copy\n",
    "# from  import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_1d_dct(i, freq, L):\n",
    "    result = math.cos(math.pi * freq * (i + 0.5) / L) / math.sqrt(L)\n",
    "    if freq == 0: \n",
    "        return result \n",
    "    else: \n",
    "        return result * math.sqrt(2) \n",
    "def get_dct_weights( width, height, channel, fidx_u= [0,1,0,5,2,0,2,0,0,6,0,4,6,3,2,5], fidx_v= [0,0,6,0,0,1,1,4,5,1,3,0,0,0,2,3]):\n",
    "    # width : width of input \n",
    "    # height : height of input \n",
    "    # channel : channel of input \n",
    "    # fidx_u : horizontal indices of selected fequency \n",
    "    # according to the paper, should be [0,0,6,0,0,1,1,4,5,1,3,0,0,0,2,3]\n",
    "    # fidx_v : vertical indices of selected fequency \n",
    "    # according to the paper, should be [0,1,0,5,2,0,2,0,0,6,0,4,6,3,2,5]\n",
    "    # [0,0],[0,1],[6,0],[0,5],[0,2],[1,0],[1,2],[4,0],\n",
    "    # [5,0],[1,6],[3,0],[0,4],[0,6],[0,3],[2,2],[3,5],\n",
    "    dct_weights = torch.zeros(1, channel, width, height)\n",
    "    #channel为什么是torch.Size([32, 256, 8, 8])\n",
    "    c_part = channel // len(fidx_u) \n",
    "    # split channel for multi-spectal attention \n",
    "    for i, (u_x, v_y) in enumerate(zip(fidx_u, fidx_v)): \n",
    "        for t_x in range(width): \n",
    "            for t_y in range(height): \n",
    "                dct_weights[:, i * c_part: (i+1)*c_part, t_x, t_y]\\\n",
    "                =get_1d_dct(t_x, u_x, width) * get_1d_dct(t_y, v_y, height) \n",
    "    # Eq. 7 in our paper \n",
    "    return dct_weights \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入输出相同\n",
    "\n",
    "class FcaLayer(nn.Module):\n",
    "    def __init__(self,\n",
    "                 channel,\n",
    "                 reduction,width,height):\n",
    "        super(FcaLayer, self).__init__()\n",
    "        self.register_buffer('pre_computed_dct_weights',get_dct_weights(width,height,channel).to(device))\n",
    "        #self.register_parameter('pre_computed_dct_weights',torch.nn.Parameter(get_dct_weights(width,height,channel)))\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(channel, channel // reduction, bias=False),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(channel // reduction, channel, bias=False),\n",
    "            nn.Sigmoid()\n",
    "        ).to(device)\n",
    "\n",
    "    def forward(self, x):\n",
    "        b, c, _, _ = x.size()\n",
    "        y = torch.sum(x*self.pre_computed_dct_weights,dim=(2,3)).to(device)\n",
    "        y = self.fc(y).view(b, c, 1, 1)\n",
    "        return x * y.expand_as(x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_shape(item):\n",
    "    print(item.shape)\n",
    "class Bottleneck(nn.Module):\n",
    "    expansion = 4\n",
    "\n",
    "    def __init__(self, inplanes, planes, stride=1, downsample=None, scales=4, groups=1, se=True):\n",
    "        super(Bottleneck, self).__init__()\n",
    "        self.downsample = downsample\n",
    "        self.scales = scales\n",
    "        self.groups = groups\n",
    "        self.stride = stride\n",
    "\n",
    "        outplanes = groups * planes\n",
    "\n",
    "        self.conv1 = nn.Conv2d(in_channels=inplanes, out_channels=outplanes, kernel_size=1, stride=1, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(outplanes)\n",
    "\n",
    "        self.conv2 = nn.ModuleList([nn.Conv2d(outplanes // scales, outplanes // scales,\n",
    "                                              kernel_size=3, stride=stride, padding=1, groups=groups, bias=False) for _\n",
    "                                    in range(scales - 1)])\n",
    "        # self.conv2 = nn.ModuleList([\n",
    "        #     nn.Conv2d(outplanes // scales, outplanes // scales, kernel_size=3, stride=1, padding=1, groups=groups, bias=False)\n",
    "        #     if i != (scales - 2)\n",
    "        #     else nn.Conv2d(outplanes // scales, outplanes // scales, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)\n",
    "        #     for i in range(scales - 1)\n",
    "        # ])\n",
    "        \n",
    "        self.bn2 = nn.ModuleList([nn.BatchNorm2d(outplanes // scales) for _ in range(scales - 1)])\n",
    "\n",
    "        self.conv3 = nn.Conv2d(outplanes, planes * self.expansion, kernel_size=1, stride=1, bias=False)\n",
    "        self.bn3 = nn.BatchNorm2d(planes * self.expansion)\n",
    "\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        # self.se = FcaLayer(planes * self.expansion,width,height) if se else None\n",
    "        self.fac = None\n",
    "        self.pool = nn.AvgPool2d(kernel_size=2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        identity = x\n",
    "\n",
    "        if self.downsample is not None:\n",
    "            identity = self.downsample(identity)\n",
    "\n",
    "        out = self.conv1(x)\n",
    "        out = self.bn1(out)\n",
    "        out = self.relu(out)\n",
    "\n",
    "        x_scales = torch.chunk(out, self.scales, 1)\n",
    "        #print(list(map(show_shape, x_scales)))\n",
    "        #print(x_scales[4])\n",
    "        # for index, i in enumerate(x_scales):\n",
    "        #     # for index_p, p in enumerate(i):\n",
    "        #     #     print(index, index_p)\n",
    "        #     print(index, i.shape)\n",
    "\n",
    "        \n",
    "        for i in range(self.scales - 1):\n",
    "            if i == 0:\n",
    "                y_scale = x_scales[i]\n",
    "                #print(0, y_scale.shape)\n",
    "            else:\n",
    "                #print(y_scale.shape, x_scales[i].shape)\n",
    "                y_scale = y_scale + x_scales[i]\n",
    "                \n",
    "            y_scale_reshape = y_scale\n",
    "            y_scale_reshape = self.conv2[i](y_scale)\n",
    "            y_scale_reshape = self.relu(self.bn2[i](y_scale_reshape))\n",
    "            \n",
    "            if i == 0:\n",
    "                out = y_scale_reshape\n",
    "            else:\n",
    "                #print(out.shape, y_scale_reshape.shape, y_scale.shape)\n",
    "                out = torch.cat((out, y_scale_reshape), 1)\n",
    "\n",
    "\n",
    "\n",
    "        if out.size()[2] != x_scales[self.scales - 1].size()[2]:\n",
    "            out = torch.cat((out, self.pool(x_scales[self.scales - 1])), 1)\n",
    "        elif self.scales != 1:\n",
    "            out = torch.cat((out, x_scales[self.scales - 1]), 1)\n",
    "            \n",
    "\n",
    "        out = self.conv3(out)\n",
    "        out = self.bn3(out)\n",
    "\n",
    "        #print(out.size())\n",
    "\n",
    "\n",
    "        #out = FcaLayer(channel, 16, width, height) \n",
    "        if self.fac is None:\n",
    "            channel = out.size(1)\n",
    "            width = out.size(2)\n",
    "            height = out.size(3)\n",
    "            self.fac = FcaLayer(channel, 16, width, height) \n",
    "        out = self.fac(out)\n",
    "        \n",
    "\n",
    "\n",
    "        out += identity\n",
    "        out = self.relu(out)\n",
    "\n",
    "        return out\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Res2Net(nn.Module):\n",
    "    def __init__(self, block, layers, num_classes=100, scales=4, groups=1, se=True):\n",
    "        super(Res2Net, self).__init__()\n",
    "        self.inplanes = 64\n",
    "\n",
    "        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)\n",
    "        self.bn1 = nn.BatchNorm2d(self.inplanes)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "\n",
    "        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n",
    "\n",
    "        self.layer1 = self._make_layer(block, 64, layers[0], stride=1, scales=scales, groups=groups, se=se)\n",
    "        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, scales=scales, groups=groups, se=se)\n",
    "        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, scales=scales, groups=groups, se=se)\n",
    "        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, scales=scales, groups=groups, se=se)\n",
    "\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.fc = nn.Linear(512 * block.expansion, num_classes)\n",
    "\n",
    "        for m in self.modules():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                nn.init.constant_(m.weight, 1)\n",
    "                nn.init.constant_(m.bias, 0)\n",
    "\n",
    "    def _make_layer(self, block, planes, layer, stride=1, scales=4, groups=1, se=True):\n",
    "        downsample = None\n",
    "        if stride != 1 or self.inplanes != planes * block.expansion:\n",
    "            downsample = nn.Sequential(\n",
    "                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),\n",
    "                nn.BatchNorm2d(planes * block.expansion),\n",
    "            )\n",
    "\n",
    "        layers = []\n",
    "        layers.append(block(self.inplanes, planes, stride=stride, downsample=downsample,\n",
    "                            scales=scales, groups=groups, se=se))\n",
    "        self.inplanes = planes * block.expansion\n",
    "\n",
    "        for i in range(1, layer):\n",
    "            layers.append(block(self.inplanes, planes, scales=scales, groups=groups, se=se))\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        x = self.maxpool(x)\n",
    "\n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "\n",
    "        return x\n",
    "\n",
    "\n",
    "def res2net50_se(num_classes=1000, scales=4, groups=1):\n",
    "    return Res2Net(Bottleneck, [3, 4, 6, 3], num_classes, scales, groups, se=True)\n",
    "\n",
    "def res2net18_fca(num_classes=1000, scales=4, groups=1, se=True):\n",
    "    return Res2Net(Bottleneck, [2, 2, 2, 2], num_classes, scales, groups, se)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def get_data_loader():    \n",
    "    transform_train = transforms.Compose([\n",
    "        transforms.RandomCrop(32, padding=4),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        RandAugment(),  # 使用 RandAugment\n",
    "        transforms.ToTensor(),\n",
    "        #transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "        transforms.Normalize(\n",
    "            (0.5070751592371323, 0.48654887331495095, 0.4409178433670343),\n",
    "            (0.2673342858792401, 0.2564384629170883, 0.27615047132568404)\n",
    "        )\n",
    "    ])\n",
    "    \n",
    "    transform_test = transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "    ])\n",
    "    \n",
    "\n",
    "    trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "                                             download=False, transform=transform_train)\n",
    "    trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,\n",
    "                                              shuffle=True, num_workers=2)\n",
    "\n",
    "    testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "                                            download=False, transform=transform_test)\n",
    "    testloader = torch.utils.data.DataLoader(testset, batch_size=128,\n",
    "                                             shuffle=False, num_workers=2)\n",
    "        # 实例化模型\n",
    "    return trainloader,testloader\n",
    "\n",
    "\n",
    "def plot_training_results(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs):\n",
    "    # Convert PyTorch tensors to NumPy arrays\n",
    "    train_losses = np.array(train_losses)\n",
    "    train_top1_accs = np.array(train_top1_accs)\n",
    "    train_top5_accs = np.array(train_top5_accs)\n",
    "    val_losses = np.array(val_losses)\n",
    "    val_top1_accs = np.array(val_top1_accs)\n",
    "    val_top5_accs = np.array(val_top5_accs)\n",
    "\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    \n",
    "    # 绘制损失曲线\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    # 绘制损失曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(train_losses, label='Training Loss')\n",
    "    plt.plot(val_losses, label='Validation Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Training/Validation Loss Curve')\n",
    "    plt.legend()\n",
    "\n",
    "    # 绘制准确率曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(train_top1_accs, label='Training Top-1 Accuracy')\n",
    "    plt.plot(val_top1_accs, label='Validation Top-1 Accuracy')\n",
    "    plt.plot(train_top5_accs, label='Training Top-5 Accuracy')\n",
    "    plt.plot(val_top5_accs, label='Validation Top-5 Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Training/Validation Accuracy Curve')\n",
    "    plt.legend()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# 训练模型\n",
    "def train_model(model,trainloader,device,testloader, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "       \n",
    "    train_losses = []\n",
    "    train_top1_accs = []\n",
    "    train_top5_accs = []\n",
    "    val_losses = []\n",
    "    val_top1_accs = []\n",
    "    val_top5_accs = []\n",
    "\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    " \n",
    "    for epoch in range(num_epochs):\n",
    "        print('第 {}/{} 轮训练'.format(epoch, num_epochs - 1))\n",
    "        print('-' * 10)\n",
    "\n",
    "        # 每个epoch都有训练和验证阶段\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                model.train()  # 设置模型为训练模式\n",
    "            else:\n",
    "                model.eval()   # 设置模型为评估模式\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "            running_top1_corrects = 0\n",
    "            running_top5_corrects = 0\n",
    "\n",
    "            # 遍历数据\n",
    "            for inputs, labels in (trainloader if phase == 'train' else testloader):\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # 零化参数梯度\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # 前向传播\n",
    "                # 只在训练阶段追踪历史\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # 计算 top-1 和 top-5 准确率\n",
    "                    _, top5_preds = torch.topk(outputs, 5, dim=1)\n",
    "                    top1_correct = torch.sum(preds == labels.data)\n",
    "                    top5_correct = torch.sum(top5_preds == labels.view(-1, 1))\n",
    "\n",
    "                    # 只有在训练阶段反向传播+优化\n",
    "                    if phase == 'train':\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # 统计\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += top1_correct\n",
    "                running_top1_corrects += top1_correct\n",
    "                running_top5_corrects += top5_correct\n",
    "\n",
    "            if phase == 'train':\n",
    "                scheduler.step()\n",
    "\n",
    "            epoch_loss = running_loss / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top1_acc = running_top1_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "            epoch_top5_acc = running_top5_corrects.double() / len(trainloader.dataset if phase == 'train' else testloader.dataset)\n",
    "\n",
    "            print('{} 损失: {:.4f} Top-1 准确率: {:.4f} Top-5 准确率: {:.4f}'.format(\n",
    "                phase, epoch_loss, epoch_top1_acc, epoch_top5_acc))\n",
    "            \n",
    "            # 保存损失和准确率\n",
    "            if phase == 'train':\n",
    "                train_losses.append(epoch_loss)\n",
    "                train_top1_accs.append(epoch_top1_acc)\n",
    "                train_top5_accs.append(epoch_top5_acc)\n",
    "            else:\n",
    "                val_losses.append(epoch_loss)\n",
    "                val_top1_accs.append(epoch_top1_acc)\n",
    "                val_top5_accs.append(epoch_top5_acc)\n",
    "\n",
    "            # 深度复制模型\n",
    "            if phase == 'val' and epoch_top1_acc > best_acc:\n",
    "                best_acc = epoch_top1_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print('训练完成，耗时 {:.0f}m {:.0f}s'.format(\n",
    "        time_elapsed // 60, time_elapsed % 60))\n",
    "    print('最佳验证集准确率: {:4f}'.format(best_acc))\n",
    "\n",
    "    # 加载最佳模型权重\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    \n",
    "    # 绘制训练结果曲线\n",
    "\n",
    "    return model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "#一些参数的设置\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "learning_rate=0.01\n",
    "momentum=0.9\n",
    "weight_decay=0.0001\n",
    "batch_size=64\n",
    "epochs=50\n",
    "data_path='./data'\n",
    "model_name='Res2net_FcaNet'+'epoch='+str(epochs)\n",
    "print(device)\n",
    "model= res2net18_fca().to(device)\n",
    "criterion=nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum, weight_decay=weight_decay)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n",
      "第 0/49 轮训练\n",
      "----------\n",
      "train 损失: 4.4426 Top-1 准确率: 0.0562 Top-5 准确率: 0.1802\n",
      "val 损失: 4.4086 Top-1 准确率: 0.0545 Top-5 准确率: 0.1844\n",
      "\n",
      "第 1/49 轮训练\n",
      "----------\n",
      "train 损失: 3.9412 Top-1 准确率: 0.1080 Top-5 准确率: 0.3108\n",
      "val 损失: 4.0871 Top-1 准确率: 0.0963 Top-5 准确率: 0.2760\n",
      "\n",
      "第 2/49 轮训练\n",
      "----------\n",
      "train 损失: 3.6698 Top-1 准确率: 0.1483 Top-5 准确率: 0.3809\n",
      "val 损失: 3.9748 Top-1 准确率: 0.1022 Top-5 准确率: 0.3059\n",
      "\n",
      "第 3/49 轮训练\n",
      "----------\n",
      "train 损失: 3.4964 Top-1 准确率: 0.1704 Top-5 准确率: 0.4242\n",
      "val 损失: 3.6878 Top-1 准确率: 0.1428 Top-5 准确率: 0.3870\n",
      "\n",
      "第 4/49 轮训练\n",
      "----------\n",
      "train 损失: 3.3504 Top-1 准确率: 0.1979 Top-5 准确率: 0.4668\n",
      "val 损失: 3.5743 Top-1 准确率: 0.1678 Top-5 准确率: 0.4153\n",
      "\n",
      "第 5/49 轮训练\n",
      "----------\n",
      "train 损失: 3.2296 Top-1 准确率: 0.2154 Top-5 准确率: 0.4955\n",
      "val 损失: 3.5137 Top-1 准确率: 0.1810 Top-5 准确率: 0.4232\n",
      "\n",
      "第 6/49 轮训练\n",
      "----------\n",
      "train 损失: 3.1136 Top-1 准确率: 0.2377 Top-5 准确率: 0.5249\n",
      "val 损失: 3.5260 Top-1 准确率: 0.1797 Top-5 准确率: 0.4350\n",
      "\n",
      "第 7/49 轮训练\n",
      "----------\n",
      "train 损失: 3.0130 Top-1 准确率: 0.2575 Top-5 准确率: 0.5508\n",
      "val 损失: 3.2963 Top-1 准确率: 0.2166 Top-5 准确率: 0.4835\n",
      "\n",
      "第 8/49 轮训练\n",
      "----------\n",
      "train 损失: 2.9132 Top-1 准确率: 0.2747 Top-5 准确率: 0.5747\n",
      "val 损失: 3.3888 Top-1 准确率: 0.2017 Top-5 准确率: 0.4853\n",
      "\n",
      "第 9/49 轮训练\n",
      "----------\n",
      "train 损失: 2.8276 Top-1 准确率: 0.2898 Top-5 准确率: 0.5952\n",
      "val 损失: 3.0669 Top-1 准确率: 0.2439 Top-5 准确率: 0.5435\n",
      "\n",
      "第 10/49 轮训练\n",
      "----------\n",
      "train 损失: 2.7437 Top-1 准确率: 0.3066 Top-5 准确率: 0.6119\n",
      "val 损失: 3.1576 Top-1 准确率: 0.2526 Top-5 准确率: 0.5272\n",
      "\n",
      "第 11/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6739 Top-1 准确率: 0.3178 Top-5 准确率: 0.6297\n",
      "val 损失: 3.0793 Top-1 准确率: 0.2548 Top-5 准确率: 0.5383\n",
      "\n",
      "第 12/49 轮训练\n",
      "----------\n",
      "train 损失: 2.6011 Top-1 准确率: 0.3334 Top-5 准确率: 0.6455\n",
      "val 损失: 2.9889 Top-1 准确率: 0.2715 Top-5 准确率: 0.5614\n",
      "\n",
      "第 13/49 轮训练\n",
      "----------\n",
      "train 损失: 2.5429 Top-1 准确率: 0.3479 Top-5 准确率: 0.6607\n",
      "val 损失: 2.9314 Top-1 准确率: 0.2915 Top-5 准确率: 0.5808\n",
      "\n",
      "第 14/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4889 Top-1 准确率: 0.3557 Top-5 准确率: 0.6711\n",
      "val 损失: 2.8778 Top-1 准确率: 0.2954 Top-5 准确率: 0.5842\n",
      "\n",
      "第 15/49 轮训练\n",
      "----------\n",
      "train 损失: 2.4311 Top-1 准确率: 0.3701 Top-5 准确率: 0.6810\n",
      "val 损失: 2.7339 Top-1 准确率: 0.3192 Top-5 准确率: 0.6172\n",
      "\n",
      "第 16/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3857 Top-1 准确率: 0.3801 Top-5 准确率: 0.6918\n",
      "val 损失: 2.8590 Top-1 准确率: 0.2998 Top-5 准确率: 0.5923\n",
      "\n",
      "第 17/49 轮训练\n",
      "----------\n",
      "train 损失: 2.3400 Top-1 准确率: 0.3896 Top-5 准确率: 0.7038\n",
      "val 损失: 2.9454 Top-1 准确率: 0.2972 Top-5 准确率: 0.5836\n",
      "\n",
      "第 18/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2890 Top-1 准确率: 0.3963 Top-5 准确率: 0.7155\n",
      "val 损失: 2.7571 Top-1 准确率: 0.3214 Top-5 准确率: 0.6143\n",
      "\n",
      "第 19/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2447 Top-1 准确率: 0.4080 Top-5 准确率: 0.7233\n",
      "val 损失: 2.6879 Top-1 准确率: 0.3309 Top-5 准确率: 0.6318\n",
      "\n",
      "第 20/49 轮训练\n",
      "----------\n",
      "train 损失: 2.2102 Top-1 准确率: 0.4165 Top-5 准确率: 0.7289\n",
      "val 损失: 2.6674 Top-1 准确率: 0.3300 Top-5 准确率: 0.6367\n",
      "\n",
      "第 21/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1654 Top-1 准确率: 0.4265 Top-5 准确率: 0.7385\n",
      "val 损失: 2.4954 Top-1 准确率: 0.3623 Top-5 准确率: 0.6754\n",
      "\n",
      "第 22/49 轮训练\n",
      "----------\n",
      "train 损失: 2.1246 Top-1 准确率: 0.4328 Top-5 准确率: 0.7469\n",
      "val 损失: 2.5685 Top-1 准确率: 0.3522 Top-5 准确率: 0.6613\n",
      "\n",
      "第 23/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0984 Top-1 准确率: 0.4396 Top-5 准确率: 0.7521\n",
      "val 损失: 2.4903 Top-1 准确率: 0.3636 Top-5 准确率: 0.6755\n",
      "\n",
      "第 24/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0630 Top-1 准确率: 0.4479 Top-5 准确率: 0.7594\n",
      "val 损失: 2.4995 Top-1 准确率: 0.3669 Top-5 准确率: 0.6733\n",
      "\n",
      "第 25/49 轮训练\n",
      "----------\n",
      "train 损失: 2.0203 Top-1 准确率: 0.4581 Top-5 准确率: 0.7669\n",
      "val 损失: 2.4683 Top-1 准确率: 0.3772 Top-5 准确率: 0.6822\n",
      "\n",
      "第 26/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9870 Top-1 准确率: 0.4632 Top-5 准确率: 0.7747\n",
      "val 损失: 2.3791 Top-1 准确率: 0.3938 Top-5 准确率: 0.6989\n",
      "\n",
      "第 27/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9534 Top-1 准确率: 0.4726 Top-5 准确率: 0.7794\n",
      "val 损失: 2.4045 Top-1 准确率: 0.3891 Top-5 准确率: 0.6950\n",
      "\n",
      "第 28/49 轮训练\n",
      "----------\n",
      "train 损失: 1.9239 Top-1 准确率: 0.4788 Top-5 准确率: 0.7869\n",
      "val 损失: 2.3702 Top-1 准确率: 0.3997 Top-5 准确率: 0.7078\n",
      "\n",
      "第 29/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8893 Top-1 准确率: 0.4893 Top-5 准确率: 0.7916\n",
      "val 损失: 2.3968 Top-1 准确率: 0.3899 Top-5 准确率: 0.7022\n",
      "\n",
      "第 30/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8745 Top-1 准确率: 0.4910 Top-5 准确率: 0.7950\n",
      "val 损失: 2.4472 Top-1 准确率: 0.3921 Top-5 准确率: 0.6933\n",
      "\n",
      "第 31/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8523 Top-1 准确率: 0.4961 Top-5 准确率: 0.7986\n",
      "val 损失: 2.3974 Top-1 准确率: 0.3987 Top-5 准确率: 0.7017\n",
      "\n",
      "第 32/49 轮训练\n",
      "----------\n",
      "train 损失: 1.8104 Top-1 准确率: 0.5065 Top-5 准确率: 0.8067\n",
      "val 损失: 2.4935 Top-1 准确率: 0.3845 Top-5 准确率: 0.6845\n",
      "\n",
      "第 33/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7969 Top-1 准确率: 0.5083 Top-5 准确率: 0.8087\n",
      "val 损失: 2.2887 Top-1 准确率: 0.4142 Top-5 准确率: 0.7178\n",
      "\n",
      "第 34/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7470 Top-1 准确率: 0.5194 Top-5 准确率: 0.8185\n",
      "val 损失: 2.3344 Top-1 准确率: 0.4127 Top-5 准确率: 0.7097\n",
      "\n",
      "第 35/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7303 Top-1 准确率: 0.5234 Top-5 准确率: 0.8222\n",
      "val 损失: 2.6373 Top-1 准确率: 0.3598 Top-5 准确率: 0.6567\n",
      "\n",
      "第 36/49 轮训练\n",
      "----------\n",
      "train 损失: 1.7055 Top-1 准确率: 0.5296 Top-5 准确率: 0.8258\n",
      "val 损失: 2.3586 Top-1 准确率: 0.4164 Top-5 准确率: 0.7007\n",
      "\n",
      "第 37/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6887 Top-1 准确率: 0.5321 Top-5 准确率: 0.8302\n",
      "val 损失: 2.2269 Top-1 准确率: 0.4286 Top-5 准确率: 0.7380\n",
      "\n",
      "第 38/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6645 Top-1 准确率: 0.5406 Top-5 准确率: 0.8326\n",
      "val 损失: 2.3276 Top-1 准确率: 0.4161 Top-5 准确率: 0.7208\n",
      "\n",
      "第 39/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6448 Top-1 准确率: 0.5465 Top-5 准确率: 0.8376\n",
      "val 损失: 2.2499 Top-1 准确率: 0.4340 Top-5 准确率: 0.7270\n",
      "\n",
      "第 40/49 轮训练\n",
      "----------\n",
      "train 损失: 1.6165 Top-1 准确率: 0.5509 Top-5 准确率: 0.8444\n",
      "val 损失: 2.3617 Top-1 准确率: 0.4120 Top-5 准确率: 0.7170\n",
      "\n",
      "第 41/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5947 Top-1 准确率: 0.5576 Top-5 准确率: 0.8452\n",
      "val 损失: 2.3066 Top-1 准确率: 0.4175 Top-5 准确率: 0.7211\n",
      "\n",
      "第 42/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5794 Top-1 准确率: 0.5596 Top-5 准确率: 0.8479\n",
      "val 损失: 2.3065 Top-1 准确率: 0.4266 Top-5 准确率: 0.7203\n",
      "\n",
      "第 43/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5592 Top-1 准确率: 0.5659 Top-5 准确率: 0.8511\n",
      "val 损失: 2.2763 Top-1 准确率: 0.4283 Top-5 准确率: 0.7241\n",
      "\n",
      "第 44/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5364 Top-1 准确率: 0.5724 Top-5 准确率: 0.8549\n",
      "val 损失: 2.2646 Top-1 准确率: 0.4277 Top-5 准确率: 0.7273\n",
      "\n",
      "第 45/49 轮训练\n",
      "----------\n",
      "train 损失: 1.5203 Top-1 准确率: 0.5755 Top-5 准确率: 0.8585\n",
      "val 损失: 2.2732 Top-1 准确率: 0.4377 Top-5 准确率: 0.7279\n",
      "\n",
      "第 46/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4914 Top-1 准确率: 0.5816 Top-5 准确率: 0.8625\n",
      "val 损失: 2.4480 Top-1 准确率: 0.3990 Top-5 准确率: 0.7019\n",
      "\n",
      "第 47/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4880 Top-1 准确率: 0.5828 Top-5 准确率: 0.8647\n",
      "val 损失: 2.2534 Top-1 准确率: 0.4282 Top-5 准确率: 0.7399\n",
      "\n",
      "第 48/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4705 Top-1 准确率: 0.5878 Top-5 准确率: 0.8663\n",
      "val 损失: 2.1558 Top-1 准确率: 0.4590 Top-5 准确率: 0.7553\n",
      "\n",
      "第 49/49 轮训练\n",
      "----------\n",
      "train 损失: 1.4562 Top-1 准确率: 0.5926 Top-5 准确率: 0.8673\n",
      "val 损失: 2.1431 Top-1 准确率: 0.4635 Top-5 准确率: 0.7537\n",
      "\n",
      "训练完成，耗时 31m 47s\n",
      "最佳验证集准确率: 0.463500\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    " ## 修改模型\n",
    "net = res2net18_fca().to(device)\n",
    " # 定义损失函数和优化器\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)\n",
    "scheduler = lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.1)\n",
    "trainloader,testloader = get_data_loader()\n",
    "model,(train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs) = train_model(net,trainloader,device,testloader, criterion, optimizer, scheduler, epochs)  \n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_cpu_numpy(tensor):\n",
    "    return tensor.cpu().numpy()\n",
    "def to_cpu(list_):\n",
    "    if isinstance(list_, list):\n",
    "        return list(map(to_cpu_numpy,list_))\n",
    "    else:\n",
    "        return to_cpu_numpy(list_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training_results(train_losses, to_cpu(train_top1_accs), to_cpu(train_top5_accs), val_losses, to_cpu(val_top1_accs), to_cpu(val_top5_accs))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"str\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[67], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msave_log\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_losses\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_top1_accs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_top5_accs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_losses\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_top1_accs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_top5_accs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32me:\\学习资料\\大三\\深度学习\\期末大作业\\ZhaoKangming\\tools.py:163\u001b[0m, in \u001b[0;36msave_log\u001b[1;34m(modelname, train_acc, test_acc, train_loss, test_loss, train_top5_correct, test_top5_correct, output_dir)\u001b[0m\n\u001b[0;32m    161\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m modelname\u001b[38;5;241m==\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    162\u001b[0m     modelname\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munnamed model\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m--> 163\u001b[0m output_dir\u001b[38;5;241m=\u001b[39m\u001b[43moutput_dir\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241m+\u001b[39mmodelname\n\u001b[0;32m    164\u001b[0m \u001b[38;5;66;03m# Create directory if it doesn't exist.\u001b[39;00m\n\u001b[0;32m    165\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mexists(output_dir):\n",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"str\") to list"
     ]
    }
   ],
   "source": [
    "save_log(model_name, model, train_losses, train_top1_accs, train_top5_accs, val_losses, val_top1_accs, val_top5_accs)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
